A rigorous introduction to probability theory: Lecture 2 with Michal Fabinger

We're excited to host a short course of 4 lectures on probability theory:

These lectures by Michal Fabinger introduce basic concepts of probability theory in an intuitive yet rigorous way. This material should later help the participants understand scientific articles that use probability theory and statistics. Such knowledge is useful both for
machine learning and data science practitioners and for those on an
academic path (undergraduates, graduate students, postdocs, or faculty members). The content is similar to the corresponding course at the Acalonia school.

📌 Lecture 2: A rigorous introduction to probability theory 2
Sigma-algebras for events. Borel sigma-algebras for events
corresponding to continuous sample spaces. Random variables. Examples of random variables.

👉 Lecturer: Michal Fabinger

system for a world where location does not matter. The school provides a straightforward way for talented people from developed and developing countries to improve their skills for their current jobs,
International Finance, and Development Economics.

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MLT (Machine Learning Tokyo)

[unuuu]

Ah I thank him so much for reading out the questions.

[magaraedson]

Anyone to kindly share the link for the first lecture? I seem not to find it!